Perturbation and Repository based Diversified Cuckoo Search in reconstruction of Gene Regulatory Network: A new Cuckoo Search approach
Tài liệu tham khảo
Assad, 2018, A hybrid harmony search and simulated annealing algorithm for continuous optimization, Inf. Sci., 450, 246, 10.1016/j.ins.2018.03.042
Margolin, 2006, ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinform., 7, S7, 10.1186/1471-2105-7-S1-S7
Gherboudj, 2012, Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm, Int. J. Bio Inspir. Comput., 4, 229, 10.1504/IJBIC.2012.048063
Pierce, 2012
A. Khan, A. Dutta, G. Saha, R.K. Pal, A hybrid methodology for the Reverse engineering of gene regulatory networks, In 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8.
Paul, 2019, Optimized time-lag differential method for constructing gene regulatory network, Inf. Sci., 478, 222, 10.1016/j.ins.2018.11.019
Ristevski, 2013, A survey of models for inference of gene regulatory networks, Nonlinear Anal. Model. Control, 18, 444, 10.15388/NA.18.4.13972
Lewin, 1994
Wolpert, 1997, No free lunch theorems for optimization, IEEE Trans. Evolut. Comput., 1, 67, 10.1109/4235.585893
Delgado, 2019, Computational methods for gene regulatory networks reconstruction and analysis: a review, Artif. Intell. Med., 95, 133, 10.1016/j.artmed.2018.10.006
G. Hall, Pearson’s correlation coefficient, Other words, 1, 9, 2015.
Sun, 2019, Differential evolution with gaussian mutation and dynamic parameter adjustment, Soft Comput., 23, 1615, 10.1007/s00500-017-2885-z
Wang, 2016, Chaotic cuckoo search, Soft Comput., 20, 3349, 10.1007/s00500-015-1726-1
Gatta, 2008, Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering, Genome Res., 18, 939, 10.1101/gr.073601.107
H. Wang, C. Li, Y. Liu, S. Zeng, A hybrid particle swarm algorithm with cauchy mutation, in: Proceedings of the 2007 IEEE Swarm Intelligence Symposium, 2007, pp. 356–360.
Sharifi-Noghabi, 2017, A novel mutation operator based on the union of fitness and design spaces information for differential evolution, Soft Comput., 21, 6555, 10.1007/s00500-016-2359-8
Chen, 2018, Bayesian data fusion of gene expression and histone modification profiles for inference of gene regulatory network, IEEE ACM Trans. Comput. Biol. Bioinform., 17, 516, 10.1109/TCBB.2018.2869590
Boveiri, 2020, An enhanced cuckoo optimization algorithm for task graph scheduling in cluster-computing systems, Soft Comput., 24, 10075, 10.1007/s00500-019-04520-3
Cantone, 2009, A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches, Cell, 137, 172, 10.1016/j.cell.2009.01.055
García, 2021, A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem, Appl. Soft Comput., 102, 10.1016/j.asoc.2020.107077
Rahaman, 2021, An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm, Expert Syst. Appl., 174, 10.1016/j.eswa.2021.114633
Kolen, 2001
Zhao, 2018, Modified cuckoo search algorithm to solve economic power dispatch optimization problems, IEEE CAA J. Autom. Sin., 5, 794, 10.1109/JAS.2018.7511138
Vohradsky, 2001, Neural model of the genetic network, J. Biol. Chem., 276, 36168, 10.1074/jbc.M104391200
Yu, 2004, Advances to bayesian network inference for generating causal networks from observational biological data, Bioinformatics, 20, 3594, 10.1093/bioinformatics/bth448
Kentzoglanakis, 2011, A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures, IEEE ACM Trans. Comput. Biol. Bioinform., 9, 358, 10.1109/TCBB.2011.87
Raza, 2016, Recurrent neural network based hybrid model for reconstructing gene regulatory network, Comput. Biol. Chem., 64, 322, 10.1016/j.compbiolchem.2016.08.002
Palafox, 2012, Reverse engineering of gene regulatory networks using dissipative particle swarm optimization, IEEE Trans. Evolut. Comput., 17, 577, 10.1109/TEVC.2012.2218610
Wang, 2016, Nearest neighbour cuckoo search algorithm with probabilistic mutation, Appl. Soft Comput., 49, 498, 10.1016/j.asoc.2016.08.021
Liu, 2020, Reconstructing gene regulatory networks via memetic algorithm and LASSO based on recurrent neural networks, Soft Comput., 24, 4205, 10.1007/s00500-019-04185-y
Kordmahalleh, 2017, Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network, BioData Min., 10, 29, 10.1186/s13040-017-0146-4
Ali, 2011, Improving the performance of differential evolution algorithm using cauchy mutation, Soft Comput., 15, 991, 10.1007/s00500-010-0655-2
El Aziz, 2018, Modified cuckoo search algorithm with rough sets for feature selection, Neural Comput. Appl., 29, 925, 10.1007/s00521-016-2473-7
Marichelvam, 2014, Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize make span, Appl. Soft Comput., 19, 93, 10.1016/j.asoc.2014.02.005
Naik, 2016, A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition, Appl. Soft Comput., 38, 661, 10.1016/j.asoc.2015.10.039
M. Zhou, Z. Zhao, C. Xiong, Q. Kang, An opposition-based particle swarm optimization algorithm for noisy environments, in: Poceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), 2018, pp. 1–6.
Eisen, 1999, DNA arrays for analysis of gene expression, 179, 10.1016/S0076-6879(99)03014-1
N. Higashi, H. Iba, Particle swarm optimization with gaussian mutation, in: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS'03), 2003, pp. 72–79.
N. Morshed, M. Chetty, Information theoretic dynamic bayesian network approach for reconstructing genetic networks, Proc. AIA (AIA 2011), 2011, pp. 236–243.
N. Morshed, M. Chetty, Reconstructing genetic networks with concurrent representation of instantaneous and time-delayed interactions, in: Poceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1840–1847.
N.M. Nawi, A. Khan, M.Z. Rehman, A new cuckoo search based levenberg-marquardt (CSLM) algorithm, in: Poceedings of the international conference on computational science and its applications, Springer, Berlin, Heidelberg, 2013, pp. 438–451.
P.A. Mundra, J. Zheng, M. Niranjan, R.E. Welsch, J.C. Rajapakse, Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression, in: Poceedings of the International Symposium on Bioinformatics Research and Applications, Springer, Berlin, Heidelberg, 2013, pp. 64–75.
Zoppoli, 2010, Time Delay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach, BMC Bioinform., 11, 154, 10.1186/1471-2105-11-154
Dasgupta, 2015, A discrete inter-species cuckoo search for flow shop scheduling problems, Comput. Oper. Res., 60, 111, 10.1016/j.cor.2015.01.005
P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.P. Chen, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, KanGAL report, 2005, 2005005.
Werbos, 1990, Backpropagation through time: what it does and how to do it, Proc. IEEE, 78, 1550, 10.1109/5.58337
Salgotra, 2018, New cuckoo search algorithms with enhanced exploration and exploitation properties, Expert Syst. Appl., 95, 384, 10.1016/j.eswa.2017.11.044
Jensi, 2016, An enhanced particle swarm optimization with levy flight for global optimization, Appl. Soft Comput., 43, 248, 10.1016/j.asoc.2016.02.018
Mansson, 2004, Pearson correlation analysis of microarray data allows for the identification of genetic targets for early B-cell factor, J. Biol. Chem., 279, 17905, 10.1074/jbc.M400589200
Xu, 2007, Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization, Neural Netw., 20, 917, 10.1016/j.neunet.2007.07.002
Xu, 2007, Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization, IEEE ACM Trans. Comput. Biol. Bioinform., 4, 681, 10.1109/TCBB.2007.1057
Biswas, 2016, Neural model of gene regulatory network: a survey on supportive meta-heuristics, Theory Biosci., 135, 1, 10.1007/s12064-016-0224-z
Biswas, 2018, A Bi-objective RNN model to reconstruct gene regulatory network: a modified multi-objective simulated annealing approach, IEEE ACM Trans. Comput. Biol. Bioinform., 15, 2053, 10.1109/TCBB.2017.2771360
Biswas, 2020, Multi-objective simulated annealing variants to infer gene regulatory network: a comparative study, IEEE ACM Trans. Comput. Biol. Bioinform., 276
Dhabal, 2017, An efficient gbest-guided cuckoo search algorithm for higher order two channel filter bank design, Swarm Evolut. Comput., 33, 68, 10.1016/j.swevo.2016.10.003
Das, 2016, Recent advances in differential evolution–an updated survey, Swarm Evolut. Comput., 27, 1, 10.1016/j.swevo.2016.01.004
Kauffman, 2003, Random boolean network models and the yeast transcriptional network, Proc. Natl. Acad. Sci. U.S.A., 100, 14796, 10.1073/pnas.2036429100
Mirjalili, 2017, Chaotic gravitational constants for the gravitational search algorithm, Appl. Soft Comput., 53, 407, 10.1016/j.asoc.2017.01.008
Rahnamayan, 2008, Opposition-based differential evolution, IEEE Trans. Evolut. Comput., 12, 64, 10.1109/TEVC.2007.894200
Mandal, 2017, Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm, J. Bioinform. Comput. Biol., 15, 10.1142/S0219720017500160
Thepphakorn, 2020, Performance improvement strategies on cuckoo search algorithms for solving the university course timetabling problem, Expert Syst. Appl., 161, 10.1016/j.eswa.2020.113732
Mlakar, 2016, Hybrid self-adaptive cuckoo search for global optimization, Swarm Evolut. Comput., 29, 47, 10.1016/j.swevo.2016.03.001
Li, 2015, Modified cuckoo search algorithm with self-adaptive parameter method, Inf. Sci., 298, 80, 10.1016/j.ins.2014.11.042
Li, 2016, A particle swarm inspired cuckoo search algorithm for real parameter optimization, Soft Comput., 20, 1389, 10.1007/s00500-015-1594-8
X.S. Yang, S. Deb, Cuckoo search via Levy flights, in: Poceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009, 59, pp. 210–214.
Yao, 1999, Evolving artificial neural networks, Proc. IEEE, 87, 1423, 10.1109/5.784219
Cai, 2021, Unified integration of many-objective optimization algorithm based on temporary offspring for software defects prediction, Swarm Evolut. Comput., 63, 10.1016/j.swevo.2021.100871
Cai, 2021, A sharding scheme based many-objective optimization algorithm for enhancing security in blockchain-enabled industrial internet of things, IEEE Trans. Ind. Inform., 17, 7650, 10.1109/TII.2021.3051607
Cai, 2020, A multicloud-model-based many-objective intelligent algorithm for efficient task scheduling in internet of things, IEEE Internet Things J., 8, 9645, 10.1109/JIOT.2020.3040019
Yang, 2013, Firefly algorithm: recent advances and applications, Int. J. Swarm Intell., 1, 36, 10.1504/IJSI.2013.055801
Zhou, 2013, An improved cuckoo search algorithm for solving planar graph coloring problem, Appl. Math. Inf. Sci., 7, 785, 10.12785/amis/070249
Li, 2015, The max-min high-order dynamic Bayesian network for learning gene regulatory networks with time-delayed regulations, IEEE ACM Trans. Comput. Biol. Bioinform., 13, 792, 10.1109/TCBB.2015.2474409
Yin, 2021, Parameter identification of DC arc models using chaotic quantum cuckoo search, Appl. Soft Comput., 108, 10.1016/j.asoc.2021.107451